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2023 IEEE EMBS R9 Conference, EMBS R9 2023

作者： Gonzalez-Lujan, F.J. Osuna-Carrasco, L.P. De La Torre-Valdovinos, B. Aguilar-Garcia, I.G. Toro-Castillo, C. Trevino, M. CUCEI University of Guadalajara Translational Bioengineering Department Mexico Master of Science in Bioengineering and Intelligent Computing Program CUCEI Campus at University of Guadalajara Jalisco44430 Mexico CUCS Molecular Biology and Genomics Department University of Guadalajara Mexico

ISBN: (纸本)9798350381092

The proposed study proves the option to have a different treatment displayed on quantitative and qualitative parameters of kinematics and pain tests, by experiments with mice groups during walk and mobility analysis with kinematics parameters and pain sensation using Von Frey filaments in acute osteoarthritis knee (AOAK) model. This would allow us to have enough information to recommend or not recommend a new based treatment that can be compared to another successful treatment for osteoarthritis. Clinical Relevance - Therefore, with this study it is proposed to be able to analyze different compounds from opioids, steroids, and non-steroid anti-inflammatories (NSAIDs) like cannabinoid components, avoiding with that the dependance pathology and renal damage in patients treated with one of this kind of common treatments and thus be able to determine their viability on a in vivo model to recommend their study in instances closer to a clinical model. © 2023 IEEE.

关键词： Kinematics

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Spatial Blind Source Separation for an Age Neuroimaging Analysis

Spatial Blind Source Separation for an Age Neuroimaging Anal...

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2023 IEEE EMBS R9 Conference, EMBS R9 2023

作者： Lopez-Medina, Lorena Malinalli Paredes, Omar Lora-Castro, S. Velez-Perez, H. Romo-Vazquez, R. Morales, J. Alejandro CUCEI University of Guadalajara Biodigital Innovation Lab Mexico CUCEI University of Guadalajara Translational Bioengineering Department Mexico Science in Bioengineering and Intelligent Computing Program CUCEI Campus at University of Guadalajara JAL 44430 Mexico

ISBN: (纸本)9798350381092

As individuals age, the brain undergoes structural, functional and connectivity changes. Brain activity and anatomy do not always correspond, then evolving differently. In this regard, Independent Component Analysis (ICA) for brain parcellation proves advantageous in studying brain function with a comprehensive and flexible approach that spans across all stages of life. We proposed a method based on ICA that estimates coherent anatomical networks based on functional connectivity patterns. Clinical Relevance - The analysis of brain function across various age groups will aid in the investigation of conditions or pathologies related to alterations in functional connectivity, such as autism spectrum disorder, Alzheimers disease, major depressive disorder and epilepsy © 2023 IEEE.

关键词： Independent component analysis

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Evaluation of Pharmacological Treatment in Acute Osteo Arthrosis Model by Means of Kinematic Analysis and Pain Threshold

Evaluation of Pharmacological Treatment in Acute Osteo Arthr...

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EMBS R9 Conference, IEEE

作者： F. J. Gonzalez-Lujan L. P. Osuna-Carrasco B. De la Torre-Valdovinos I. G. Aguilar-García C. Toro-Castillo M. Treviño Translational Bioengineering Department CUCEI University of Guadalajara Master of Science in Bioengineering and Intelligent Computing program CUCEI campus at University of Guadalajara Jalisco Molecular Biology and Genomics Department CUCS University of Guadalajara Neuroscience Institute CUCBA University of Guadalajara

ISBN: (数字)9798350381092

ISBN: (纸本)9798350381108

The proposed study proves the option to have a different treatment displayed on quantitative and qualitative parameters of kinematics and pain tests, by experiments with mice groups during walk and mobility analysis with kinematics parameters and pain sensation using Von Frey filaments in acute osteoarthritis knee (AOAK) model. This would allow us to have enough information to recommend or not recommend a new based treatment that can be compared to another successful treatment for osteoarthritis. Clinical Relevance — Therefore, with this study it is proposed to be able to analyze different compounds from opioids, steroids, and non-steroid anti-inflammatories (NSAIDs) like cannabinoid components, avoiding with that the dependance pathology and renal damage in patients treated with one of this kind of common treatments and thus be able to determine their viability on a in vivo model to recommend their study in instances closer to a clinical model.

关键词： Knee Analytical models Pathology In vivo Pain Kinematics Opioids

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Quantum Fourier Transform of Atrial Fibrillation

Quantum Fourier Transform of Atrial Fibrillation

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Quantum computing and Engineering (QCE), IEEE International Conference on

作者： Yu-Ching Chen Chih-Yu Chen Tsung-Wei Huang Chia-Ho Ou Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Electrocardiograms (ECGs) are crucial for detecting cardiac diseases like atrial fibrillation (AF). Traditional analysis methods like fast Fourier transform (FFT) face challenges with increasing data complexity. This study uses Taipei Veterans General Hospital data to explore quantum Fourier transform (QFT) for ECG analysis. Results show that QFT effectively analyzes ECG signals, matching FFT performance while benefiting from quantum computing's efficiency.

关键词： Heart Quantum computing Fast Fourier transforms Hospitals High performance computing Atrial fibrillation Machine learning Electrocardiography Real-time systems Signal analysis

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eVision: Forecasting the spread of Tuberculosis in India with Deep Learning

eVision: Forecasting the spread of Tuberculosis in India wit...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Juan Zuluaga Michael Castillo Divya Syal Andres Calle Navid Shaghaghi Department of Bioengineering (BIOE) Computer Science & Engineering (CSEN) Ethical Pragmatic & Intelligent Computing (EPIC) Laboratory in collaboration with the Healthcare Innovation & Design (HID) Program Information Systems & Analytics (ISA) and Mathematics & Computer Science (MCS) Santa Clara University Santa Clara California USA

Humanity has battled tuberculosis for all of recorded history. Some studies estimate that Mycobacterium tuberculosis may have been around as long as 3 million years but it was only in 1834 when Johann Schonlein officially presented the characteristics of it. Even though the TB epidemic has touched all corners of the world, Africa and Asia are the regions that currently suffer the worst consequences. The purpose of this study is to construct a model within the eVision forecasting environment, capable of forecasting the trend of Tuberculosis cases in India, as India is the country that accounts for the largest percentage of TB cases and deaths worldwide. And being able to make predictions for India may also lead to successful results for other regions in Asia and Africa. In order to do so, this study presents different test cases that show the effectiveness of the model, varying the number of steps for each one of the data sets created. It's important to note, that these data sets are combinations of data gathered from the states with the most TB cases in India in the last years, as well as the total data for India, and supplemental data from Google Trends, as a way to facilitate the machine learning model. Even though the final results were respectable compared to past research done on India and other countries, the model nevertheless has a limitation on the number of weeks ahead which the predictions are still considered to be good; with 7 weeks being the optimal result.

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Quantum-Inspired Acceleration for Image Reconstruction on Ising Machines

Quantum-Inspired Acceleration for Image Reconstruction on Is...

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Quantum computing and Engineering (QCE), IEEE International Conference on

作者： Yen-Jui Chang Hao-Yuan Chen Ying Chang Lu Lien-Po Yu Chao-Ming Fu Ching-Ray Chang Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan Department of Physics National Taiwan University Taipei Taiwan Fujitsu Taiwan Ltd. Taipei Taiwan Department of Computer Science University of London London United Kingdom Institute for Information Industry Taipei Taiwan Fo Guang University Yilan Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan

The study investigates a quantum-inspired approach to image reconstruction using Ising machines and demonstrates its significant improvements over the contrastive divergence method in Restricted Boltzmann Machines tra... 详细信息

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

关键词： Training Quantum computing Image reconstruction

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APPLICATION OF THE DIGITAL ANNEALER UNIT IN OPTIMIZING CHEMICAL REACTION CONDITIONS FOR ENHANCED PRODUCTION YIELDS

arXiv

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arXiv 2024年

作者： Li, Shih-Cheng Wang, Pei-Hwa Su, Jheng-Wei Chiang, Wei-Yin Huang, Shih-Hsien Lin, Yen-Chu Ou, Chia-Ho Chen, Chih-Yu Insilico Medicine Taiwan Ltd. Taipei City110208 Taiwan Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Information Center Chung Yuan Christian University Taoyuan City320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City320314 Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City900392 Taiwan

Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks programming Predictive models Quantum circuit Physics Long short term memory

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Efficient Bitcoin Address Classification Using Quantum-Inspired Feature Selection

arXiv

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arXiv 2024年

作者： Sie, Ming-Fong Chang, Yen-Jui Lin, Chien-Lung Chang, Ching-Ray Liao, Shih-Wei Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei City10617 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 499 Xingzhong N. Rd. Taoyuan City320317 Taiwan Department of Physics National Taiwan University Taiwan Quantum Information Center Chung Yuan Christian University No. 499 Xingzhong N. Rd. Taoyuan City320317 Taiwan

Over 900 million Bitcoin transactions have been recorded, posing considerable challenges for machine learning regarding computation time and maintaining prediction accuracy. We propose an innovative approach using quantum-inspired algorithms implemented with Simulated Annealing and Quantum Annealing to address the challenge of local minima in solution spaces. This method efficiently identifies key features linked to mixer addresses, significantly reducing model training time. By categorizing Bitcoin addresses into six classes—exchanges, faucets, gambling, marketplaces, mixers, and mining pools—and applying supervised learning methods, our results demonstrate that feature selection with SA reduced training time by 30.3% compared to using all features in a random forest model while maintaining a 91% F1-score for mixer addresses. This highlights the potential of quantum-inspired algorithms to swiftly and accurately identify high-risk Bitcoin addresses based on transaction features. © 2024, CC BY.

关键词： Simulated annealing

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Data gap/outlier correction and treatment

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AIP Conference Proceedings 2024年第1期3034卷

作者： Juan Zuluaga Michael Castillo Divya Syal Andres Calle Navid Shaghaghi Ethical Pragmatic and Intelligent Computing (EPIC) Research Laboratory and the Healthcare Innovation and Design (HID) Program Departments of Computer Science and Engineering (CSEN) and Bioengineering (BIOE) of the School of Engineering (SoE) at Santa Clara University (SCU) in Santa Clara California USA

The prevalence of gaps and outliers within datasets presents substantial challenges, particularly in the realm of time series fore-casting and various other predictive machine learning (ML) tasks. This paper, introduces an effective technique for correcting gaps and outliers in data and validates the approach by applying it to datasets with outlier zones drawn from three diverse contexts. This innovative technique holds promising potential to enhance the performance of machine learning models by treating the data to alleviate the complications posed by these issues and in doing so contributes a valuable tool to the data science toolbox.

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